Conventional collaborative filtering (CF)–the dominant paradigm in personalized recommendation–relies primarily on implicit data collection to drive recommendations [1]}. CF leverages implicit data to determine a focal user's “nearest neighbors” [2]} and recommends items bought or consumed by the “neighbors,” but not yet by the focal user. In many large-scale production systems, implicit data take the form of browsing history, rating metadata, search queries, and social graphs. Implicit data collection relieves users of the task of explicitly rating items and can be easily automated and scaled [3]}. Implicit data are a type of BBD.
